import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report


from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from joblib import dump

class ClassIfication(object):
    def __init__(self):
        self.df = pd.read_csv('daily.csv')

    def get_conditions(self):
        df = self.df
        df['max_ratio'] = df['max_close'] / df['the_close']
        df['min_ratio'] = df['min_close'] / df['the_close']
        
        # 自动划分高低阈值
        high_return_threshold = df['max_ratio'].quantile(0.4)  # 前40%定义为高收益
        high_risk_threshold = df['min_ratio'].quantile(0.4)    # 前40%定义为高风险

        # 生成分类标签
        conditions = [
            (df['max_ratio'] >= high_return_threshold) & (df['min_ratio'] <= high_risk_threshold),
            (df['max_ratio'] >= high_return_threshold) & (df['min_ratio'] > high_risk_threshold),
            (df['max_ratio'] < high_return_threshold) & (df['min_ratio'] > high_risk_threshold),
            (df['max_ratio'] < high_return_threshold) & (df['min_ratio'] <= high_risk_threshold),
        ]

        labels = ['高收益高风险', '高收益低风险', '低收益低风险', '低收益高风险']
        df['category'] = np.select(conditions, labels, default='未知')

        # 特征选择
        features = df[
            ['eps', 'total_revenue_ps', 'undist_profit_ps', 'gross_margin', 'fcff', 'fcfe', 'tangible_asset', 'bps', 'grossprofit_margin', 'npta']
        ]

        # 标签编码
        le = LabelEncoder()
        df['category_encoded'] = le.fit_transform(df['category'])

        # 数据标准化
        scaler = StandardScaler()
        x = scaler.fit_transform(features)
        y = df['category_encoded']

        # 划分训练集和测试集
        x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=24)
        dump(scaler, 'feature_scaler.joblib')
        return x_train, x_test, y_train, y_test, le  # 返回 LabelEncoder 对象

    def knn_utils(self, x_train, x_test, y_train, y_test, le):
        knn = KNeighborsClassifier(n_neighbors=3)
        knn.fit(x_train, y_train)

        # 预测与评估
        y_pred = knn.predict(x_test)
        print(classification_report(y_test, y_pred, target_names=le.classes_))
        
        dump(knn,'knn_classifien.joblib')
        dump(le, 'label_encoder.joblib')
        
        
    
    def svc_utils(self,x_train, x_test, y_train, y_test):
        svc =SVC()
        svc.fit(x_train,y_train)
        predict = svc.predict(x_test)
        print("accuracy_score:%.4lf" % accuracy_score(predict,y_test))
        print("Classification report for classifier %s: \n %s\n" %(svc,classification_report(y_test,predict)))
        
                
        
        
if __name__ == '__main__':
    ci = ClassIfication()
    x_train, x_test, y_train, y_test, le = ci.get_conditions()  # 获取数据和 LabelEncoder
    ci.knn_utils(x_train, x_test, y_train, y_test, le)  # 调用 knn_utils 方法
    #ci.svc_utils(x_train, x_test, y_train, y_test)